.IntroductionThe ease of design of control rules, simplicity of the control strategies, lack of dependence on accurate process models and the inherent nonlinear nature of the fuzzy logic control offer tremendous possibilities in induslxial control applications. Microcontroller chips, now available with fuzzy logic instmctions, can he used to implement such controllers [I].
Control of hard disk drivesIncreased data storage densities and significant size reduction can be achieved only by increasing the tracks per inch (TPI) and the bytes per inch (BPI) which require better control techniques. Nonlinear controllers currently being used require accurate information on the process model and the noise characteristics [2]. Hence design of nonlinear controllers is based on 'ad hoc' assumptions. Fuzzy logic controllers precisely fit into this situation and hence will be.an effective alternative to these controllers. Earlier work in this context used fuzzy logic to schedule different controllers for different track segments [3] and another tried to model and compensate for one of the nonlinear phenomenon called pivot friction non-linearity present in hard disk drives [4].
3.Application of fuzzv control to hard disk drivesThis paper report results obtained on extensive application of different types of fuzzy logic controllers for hard disk drive systems and shows that very good results can he obtained. Mamdani and Sugeno type fuzzy controllers have been studied. The simulation results on typical hard disk control models show that considerable improvement in the transient response and frequency response is possible by appropriate choice of controller parameters. The Sugeno type has the additional advantage of flexibility in choosing the controller characteristics in almost any desired manner. The choice of different types of controllers for seek and track follow modes can be integrated into one controller using fuzzy logic. The plots of the step response and frequency response obtained for a typical fifth order example with very low damping and a resonant mode at 4000 Hz are shown in Figs 1 4 Improvement possible by choosing different controller parameters is also shown. The normalized mean square error in controlled output obtained has been tabulated.
4.Conclusions.From the results, it can be seen that fuzzy loEic control can be applied to hard disk 5. References 1. E. H. Mamdani, "Applications of Fuzzy Algorithms for control of simple dynamic plant,"Proceedings IEEE, No.121, pages 1585-1588,1974.Table -I Normalized Mean Squared Error(NMSE) in Output for fuzzy logic controllers P o f c o n t m I ~ " i -'Mamdani FLC Sugeno FLC PI Like (e and ce in uts) PD Like (kl*e +k2*ce) 0.2183 0.1086 1 Error alone as input 0.1866 0.1067 0.7528 Fig.l.Step Response-Mamdani FLC Fig.2. Step Response-Sugeno FLC P cnntml-K =1 PI like Ihitv Gain P e PD like -Enor alone Fig.3. Step Response -PI like FLC with Different parameters Fig.4. Campaison of frequency response Unity P control PI like FLC controlcontrol with comparative ease using simple '...
